Pallet repair data

The paletts data set contains data from a firm that recycles paletts. Paletts from warehouses are bought, repaired, and resold. (Repairing a palette typically involves replacing one or two boards.) The company has four employees who do the repairs. The employer sampled five days for each employee and recorded the number of pallets repaired.

Format

A data frame with 20 observations on the following 3 variables.

pallets

number of pallets repaired

employee

a factor with levels A B C D

day

a factor with levels day1 day2 day3 day4 day5

Source

Michael Stob, Calvin College

Examples

data(Pallets)
# Do the employees differ in the rate at which they repair pallets?
pal.lm1 <- lm(pallets ~ employee, data = Pallets) 
anova(pal.lm1)
#> Analysis of Variance Table
#> 
#> Response: pallets
#>           Df Sum Sq Mean Sq F value Pr(>F)
#> employee   3 236.95  78.983  2.4415 0.1019
#> Residuals 16 517.60  32.350               
# Now using day as a blocking variable
pal.lm2 <- lm(pallets ~ employee + day, data = Pallets) 
anova(pal.lm2)
#> Analysis of Variance Table
#> 
#> Response: pallets
#>           Df Sum Sq Mean Sq F value    Pr(>F)    
#> employee   3 236.95  78.983  24.428 2.134e-05 ***
#> day        4 478.80 119.700  37.021 1.162e-06 ***
#> Residuals 12  38.80   3.233                      
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
gf_line(pallets ~ day, data = Pallets,
    group = ~employee,
    color = ~employee) %>%
  gf_point() %>%
  gf_labs(title = "Productivity by day and employee")